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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Divya, S.B. Mary, S. Prince Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai India Mangalam College of Engineering Department of Computer Applications India

ISBN: (纸本)9798350372816

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020-2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered. © 2024 IEEE.

关键词： Network intrusion

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Biomedical signal processing for health monitoring applications: a review

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International Journal of Applied Systemic Studies 2023年第1期10卷 44-69页

作者： Sonawani, Shilpa Patil, Kailas Natarajan, Prabhu School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University India Department of Computer Engineering Vishwakarma University India Department of Computer Science DigiPen Institute of Technology Singapore

Biomedical health monitoring systems are evolving rapidly and using non-invasive and cost effective sensors. These systems can monitor physiological parameters of the body to monitor health conditions and provide feedback. These new generation systems can use advance technologies to build intelligent systems for health monitoring and timely diseases detection and diagnosis. Biomedical signal processing and analysis of the patterns in the signals plays important role in building efficient systems, life sciences and research and is rapidly expanding in this domain. This review paper is significant as no existing review paper is giving complete information on biomedical signal processing phases as a whole. It makes four contributions;first, it gives activity flow for developing biomedical signal processing systems. Second, reviews various recent applications researched and kinds of low cost, non-invasive sensors used for biomedical health monitoring. Third, categorisation and enlisting of signal processing techniques like signal sampling, segmentation, filtering, feature extraction, dimensionality reduction and machine learning techniques in healthcare domain. Fourth, it reviews challenges in using signal processing techniques and gives future perspectives which can be helpful for the researchers to use advance techniques to build intelligent systems for reliable decision making. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cost effectiveness

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Hybrid approaches for handling imbalanced structured and unstructured data

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Multimedia Tools and Applications 2024年 1-23页

作者： Akshay, M. Manu, Rishabh Raj, Rishika Suhas, K.T. Shobha, K. Department of Computer Science and Engineering Siddaganga Institute of Technology B.H Road Karnataka Tumkur572102 India

With their limitless potential for highly accurate decision-making behaviors, machine learning algorithms have emerged to influence the world of information systems. Algorithms designed for structured and unstructured data sets do not produce good results if the data set has an uneven distribution of classes (imbalanced), this is because these algorithms are designed and developed to generalize from training data samples and to produce the most accessible hypothesis that fits the data. Uneven distribution of classes in the training stage gives a low precision on the minority class(es) but a high accuracy on the majority class(es) resulting in high-cost losses or even catastrophes. To overcome this inevitability, this work aims to propose hybrid balancing techniques. Proposed hybrid approaches have been empirically evaluated on both structured and unstructured imbalanced data sets. Empirical results have shown that the proposed hybrid approaches have outperformed state-of-the-art techniques by achieving improved mean recall rates of 40% and 20% on Pima diabetes and Cerebral stroke structured data sets respectively. The proposed method has also outperformed in balancing unstructured data by generating minority class data with the similarity (measured using Structural Similarity Index Measure (SSIM)) ranging between 25%-60% on MNSIT and Fashion-MNIST data sets. The resultant balanced dataset is further evaluated using conventional classification algorithms, where the outcomes on balanced dataset have shown a performance improvement of 10% compared to imbalanced data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Optimizing Technical Analysis and Stock Price Analysis Using: BLRM-QNDNL 21

Optimizing Technical Analysis and Stock Price Analysis Using...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Kalva, Sudhakar Naganjaneyulu, Satuluri Sudhakar, K. Computer Science and Engineering Department of Information Technology Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350358230

The Stock value forecast is a significant issue to determine the future direction of the financial Markets. Many research works are carried out and design many techniques to predict stock price of Individual stocks. But, the forecast precision/ of the regular methods was lower when taking the enormous size of the dataset. To address this downside, a Novel Deep Learning based Broken-Stick Linear Regressive MapReduce Based on Quasi-Newton Deep Neural Learning (BLRM-QNDNL) method is proposed right now. BLRM-QNDNL technique targeting optimizing the parameters engaged with stock profit prediction. Quasi-Newton based Deep Neural Network Learning (Q-NDNL) is applied in the BLRM-QNDNL procedure for precisely foreseeing the stock prices dependent on the news with higher accuracy. Q-NDNL used several hidden layer in order to thoroughly examine the effects of emotions conveyed in news items on stocks with the least amount of time complexity. The BLRM-QNDNL method expands the stock price forecast performance with lower time complexity when compared with cutting edge works. The BLRM-QNDNL technique has been evaluated exploratory on metrics such as prediction accuracy, prediction time, and false positive rate with regard to different numbers of data. When compared to state-of-the-art studies, the experimental results show that the BLRM-QNDNL technique can reduce the time complexity of stock price prediction while simultaneously increasing its accuracy. The suggested BLRM-QNDNL technique reduces the false positive stock price prediction rate by 41% and 57%, respectively and decreases market price prediction time complexity by 9 percent and 29 percent relative to traditional DeepClue [1] and MFNN [2], respectively. © 2023 IEEE.

关键词： MapReduce

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Transforming optical Vernier effect into coherent microwave interference towards highly sensitive optical fiber sensing

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Photonics Research 2025年第4期13卷 875-888页

作者： RUIMIN JIE JIE HUANG CHEN ZHU Department of Electrical and Computer Engineering Missouri University of Science and Technology

The optical Vernier effect has garnered significant research attention and found widespread applications in enhancing the measurement sensitivity of optical fiber interferometric sensors. Typically, Vernier sensor interrogation involves measuring its optical spectrum across a wide wavelength range using a high-precision spectrometer. This process is further complicated by the intricate signal processing required for accurately extracting the Vernier envelope, which can inadvertently introduce errors that compromise sensing performance.

关键词： Coherent optical effects Fabry Perot interferometers Fiber optic sensors Hollow core fibers Optical fibers Sensor performance

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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A computer-Aided Diagnosis System for the Detection of Parkinson’s Disease 8th

A Computer-Aided Diagnosis System for the Detection of Pa...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Abhijith, K.P. Sarath, R. Santhosh, Partha Mohan, Jesna Abraham, Bejoy Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Kerala Trivandrum India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Trivandrum India

ISBN: (纸本)9789819721467

Parkinson’s disease (PD) is a neurological condition that worsens over time and causes accidental or uncontrolled movements, stiffness, and problems with balance and coordination. Usually, symptoms are minor, to begin with, and worsen with time. As the condition develops, individuals may have trouble speaking and moving about. They could also have mental and psychological issues, such as fatigue, sadness, sleeplessness, and cognitive impairment. The development of useful, technologically supported methods for monitoring the development of PD symptoms in everyday life has the potential to change disease assessment and hasten diagnosis. The creation of simple, technology-based techniques for tracking PD symptoms over time in daily life has the potential to revolutionize disease evaluation and speed up diagnosis. The proposed work aims to detect Parkinson’s disease from a patient’s vocal features at a very early stage. This can be achieved by training machine learning models to detect the vocal characteristics distinct in patients with PD from the data of patients who are already diagnosed with Parkinson’s Disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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